The influence of multi-morbidity and self-reported socio-economic standing on the prevalence of depression in an elderly Hong Kong population

<b>Background</b> There has been an increasing prevalence of both depression and chronic medical conditions globally but the relationship between depression and multi-morbidity is not well understood. The aim of the present study was to investigate the relationship between depression, multi-morbidity (number of chronic medical conditions, and measures of socioeconomic standing (SES) in an elderly Hong Kong population.<p></p> <b>Methods</b> Cross sectional study. Information on clinically relevant depressive symptoms, measured by the Geriatric Depression Scale (GDS), and demographic and chronic medical conditions were collected using standardized questionnaires. Information collected on SES included educational status (ES), maximum ever income (MEI), and self-perceived social standing in local community (SES-COM) and in Hong Kong generally (SES-HK). Analysis was conducted using multiple logistic regression.<p></p> <b>Results</b> Depression rates were similar in men and women (GDS caseness 8.1% vs 8.4%). Multi-morbidity of chronic medical conditions was common (40% of men and 46% of women had three or more). In the overall sample, the prevalence of depression was associated with the number of chronic medical conditions (OR 1.27; CI: 1.16–1.39). In addition, SES-HK and SES-COM were significant independent variables.<p></p> <b>Conclusion</b> In this elderly Hong Kong population, depression prevalence rose markedly with number of chronic medical conditions and SES-HK and SES-COM

Observational and Physical Classification of Supernovae

This chapter describes the current classification scheme of supernovae (SNe). This scheme has evolved over many decades and now includes numerous SN Types and sub-types. Many of these are universally recognized, while there are controversies regarding the definitions, membership and even the names of some sub-classes; we will try to review here the commonly-used nomenclature, noting the main variants when possible. SN Types are defined according to observational properties; mostly visible-light spectra near maximum light, as well as according to their photometric properties. However, a long-term goal of SN classification is to associate observationally-defined classes with specific physical explosive phenomena. We show here that this aspiration is now finally coming to fruition, and we establish the SN classification scheme upon direct observational evidence connecting SN groups with specific progenitor stars. Observationally, the broad class of Type II SNe contains objects showing strong spectroscopic signatures of hydrogen, while objects lacking such signatures are of Type I, which is further divided to numerous subclasses. Recently a class of super-luminous SNe (SLSNe, typically 10 times more luminous than standard events) has been identified, and it is discussed. We end this chapter by briefly describing a proposed alternative classification scheme that is inspired by the stellar classification system. This system presents our emerging physical understanding of SN explosions, while clearly separating robust observational properties from physical inferences that can be debated. This new system is quantitative, and naturally deals with events distributed along a continuum, rather than being strictly divided into discrete classes. Thus, it may be more suitable to the coming era where SN numbers will quickly expand from a few thousands to millions of events.Comment: Extended final draft of a chapter in the "SN Handbook". Comments most welcom

Evidence-based Kernels: Fundamental Units of Behavioral Influence

This paper describes evidence-based kernels, fundamental units of behavioral influence that appear to underlie effective prevention and treatment for children, adults, and families. A kernel is a behavior–influence procedure shown through experimental analysis to affect a specific behavior and that is indivisible in the sense that removing any of its components would render it inert. Existing evidence shows that a variety of kernels can influence behavior in context, and some evidence suggests that frequent use or sufficient use of some kernels may produce longer lasting behavioral shifts. The analysis of kernels could contribute to an empirically based theory of behavioral influence, augment existing prevention or treatment efforts, facilitate the dissemination of effective prevention and treatment practices, clarify the active ingredients in existing interventions, and contribute to efficiently developing interventions that are more effective. Kernels involve one or more of the following mechanisms of behavior influence: reinforcement, altering antecedents, changing verbal relational responding, or changing physiological states directly. The paper describes 52 of these kernels, and details practical, theoretical, and research implications, including calling for a national database of kernels that influence human behavior